Method and System for Generating a Resilience Analysis of a Real-world System

Abstract

Embodiments generate a resilience analysis of a real-world system. One such embodiment constructs a network graph representing a real-world system for providing function(s) by generating node(s) corresponding to the function(s) and generating edge(s). Each of the edge(s) links a pair of the node(s) and represents a causal dependency between respective functions corresponding to the pair of the node(s). In turn, via a first model, based on the network graph, configuration(s) of the real-world system are simulated by modifying a configuration of a function corresponding to a node of the node(s) or a causal dependency corresponding to an edge of the edge(s). Then, via the first model, based on the simulating, a resilience analysis of the real-world system is generated.

1 . A computer-implemented method for generating a resilience analysis of a real-world system, the method comprising:
 constructing a network graph representing a real-world system for providing a plurality of functions by:
 generating a plurality of nodes corresponding to the plurality of functions; and 
 generating a plurality of edges, each of the plurality of edges (i) linking a pair of the plurality of nodes and (ii) representing a causal dependency between respective functions corresponding to the pair of the plurality of nodes; 
   simulating, via a first model, based on the network graph, one or more configurations of the real-world system by:
 modifying a configuration of: (i) at least one function corresponding to a node of the plurality of nodes or (ii) at least one causal dependency corresponding to an edge of the plurality of edges; and 
   generating, via the first model, based on the simulating, a resilience analysis of the real-world system.   
     
     
         2 . The method of  claim 1 , wherein the first model is trained with data representing one or more real-world systems having common elements to the real-world system. 
     
     
         3 . The method of  claim 1 , further comprising:
 identifying, via the first model, based on the resilience analysis, one or more modifications to the real-world system to improve resilience.   
     
     
         4 . The method of  claim 3 , further comprising generating a visualization of at least one of the one or more modifications to the real-world system. 
     
     
         5 . The method of  claim 3 , wherein the one or more modifications to the real-world system are identified based on user-defined criteria including at least one of: (i) cost and (ii) prioritizing one or more functions of the plurality of functions. 
     
     
         6 . The method of  claim 5 , further comprising:
 determining, based on the user-defined criteria, a prioritized sequence of the one or more modifications to the real-world system.   
     
     
         7 . The method of  claim 3 , further comprising:
 causing at least one of the one or more modifications to be applied to the real-world system.   
     
     
         8 . The method of  claim 7 , wherein the one or more modifications include at least one of: (i) a service-level modification and (ii) an asset-level modification. 
     
     
         9 . The method of  claim 1 , wherein generating, via the first model, based on the simulating, the resilience analysis of the real-world system includes:
 determining, via the first model, one or more resilience scores representing at least one of: (i) a capacity of the real-world system to absorb the failure of the at least one of the plurality of functions, (ii) a capacity of the real-world system to recover from the failure of the at least one of the plurality of functions, and (iii) a capacity of the real-world system to adapt to the failure of the at least one of the plurality of functions.   
     
     
         10 . The method of  claim 1 , wherein the plurality of nodes represents one or more resources associated with the plurality of functions, the one or more resources including at least one of: (i) physical resources, (ii) digital resources, and (iii) personnel resources. 
     
     
         11 . The method of  claim 1 , further comprising:
 identifying, via a second model: (i) one or more external drivers and (ii) one or more causal dependencies between the one or more external drivers and one or more corresponding functions of the plurality of functions;   generating one or more nodes corresponding to the one or more external drivers identified;   generating one or more edges corresponding to the one or more causal dependencies identified; and   modifying the network graph to incorporate the one or more nodes generated and the one or more edges generated.   
     
     
         12 . The method of  claim 11 , wherein the one or more external drivers include one or more of: (i) environmental-based drivers, (ii) transport-based drivers, (iii) econometric drivers, (iv) energy-based drivers, (v) cyber-based drivers, and (vi) commodity-based drivers. 
     
     
         13 . The method of  claim 11 , wherein the identifying comprises executing, via the second model, one or more search functions on one or more respective causal networks contained in a repository. 
     
     
         14 . The method of  claim 1 , wherein at least one of the one or more configurations of the real-world system includes a disruption to a first function of the plurality of functions, the disruption causing a failure of a second function of the plurality of functions. 
     
     
         15 . The method of  claim 1 , wherein at least one of the one or more configurations of the real-world system includes one or more distinct operational conditions of the real-world system including one or more of: (i) a system workload and (ii) demand for a function. 
     
     
         16 . The method of  claim 1 , wherein the first model includes one or more of: (i) a system dynamics model, (ii) a graph theory model, (iii) a Bayesian network model, and (iv) an agent-based model. 
     
     
         17 . The method of  claim 1 , wherein the first model includes at least one of a machine learning (ML) model and an artificial intelligence (AI) model. 
     
     
         18 . The method of  claim 1 , wherein generating the plurality of nodes corresponding to the plurality of functions comprises:
 parameterizing respective functions of the plurality of functions corresponding to the plurality of nodes.   
     
     
         19 . The method of  claim 1 , further comprising:
 generating, via a third model, based on the resilience analysis, one or more performance metrics for the real-world system; and   identifying, via the third model, based on the one or more performance metrics, at least one critical component of the real-world system.   
     
     
         20 . A computer-based system for generating a resilience analysis of a real-world system, the computer-based system comprising:
 a processor; and   a memory with computer code instructions stored thereon, the processor and the memory, with the computer code instructions, being configured to cause the computer-based system to:
 construct a network graph representing a real-world system for providing a plurality of functions by:
 generating a plurality of nodes corresponding to the plurality of functions; and 
 generating a plurality of edges, each of the plurality of edges (i) linking a pair of the plurality of nodes and (ii) representing a causal dependency between respective functions corresponding to the pair of the plurality of nodes; 
 
 simulate, via a first model, based on the network graph, one or more configurations of the real-world system by:
 modifying a configuration of: (i) at least one function corresponding to a node of the plurality of nodes or (ii) at least one causal dependency corresponding to an edge of the plurality of edges; and 
 
 generate, via the first model, based on the simulating, a resilience analysis of the real-world system. 
   
     
     
         21 . A non-transitory computer program product comprising a computer-readable medium with computer code instructions stored thereon, the computer code instructions being configured, when executed by a processor, to cause an apparatus associated with the processor to:
 construct a network graph representing a real-world system for providing a plurality of functions by:
 generating a plurality of nodes corresponding to the plurality of functions; and 
 generating a plurality of edges, each of the plurality of edges (i) linking a pair of the plurality of nodes and (ii) representing a causal dependency between respective functions corresponding to the pair of the plurality of nodes; 
   simulate, via a first model, based on the network graph, one or more configurations of the real-world system by:
 modifying a configuration of: (i) at least one function corresponding to a node of the plurality of nodes or (ii) at least one causal dependency corresponding to an edge of the plurality of edges; and 
   generate, via the first model, based on the simulating, a resilience analysis of the real-world system.